Papers with Code 아카이브
Coreference Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
- 154
Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities.
Example:
+-----------+
| |
I voted for Obama because he was most aligned with my values", she said.
| | |
+-------------------------------------------------+------------+
"I", "my", and "she" belong to the same cluster and "Obama" and "he" belong to the same cluster.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/seunghwan1228/Transfomer-MachineTranslation
https://github.com/sh951011/Attention-Implementation
https://github.com/shahrukhx01/ocr-test
https://github.com/shaikhzhas/bert
https://github.com/shaneding/bilm-tf-experimentation
https://github.com/shanybar/event_entity_coref_ecb_plus
Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution
https://github.com/shao-chi/ImageCaption
https://github.com/sharejing/BiPaR
https://github.com/shayneobrien/coreference-resolution
https://github.com/shelleyHLX/bilm_EMLo
https://github.com/sherinaseri/bert
https://github.com/shivamraval98/multitask-t5_ae
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/shouvikcirca/Transformer
https://github.com/shouvikcirca/Transformer_MiniProject
https://github.com/shreyash8/English_to_Hindi_NMT
https://github.com/shreyashankar/gpt3-sandbox
https://github.com/shtoshni/fast-coref
Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks
https://github.com/shuaishuaij/Machine-Translation
https://github.com/shyam-skk/Parallel_CNN_Transfromer
https://github.com/simonjisu/annotated-transformer-kr
https://github.com/simran-arora/focus
Ask Me Anything: A simple strategy for prompting language models
https://github.com/simran-arora/privacy_fm
Ask Me Anything: A simple strategy for prompting language models
https://github.com/siramatu/brigade-visualizer
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/sjyttkl/Transformer_learning
https://github.com/skoltech-nlp/rudetoxifier
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/skoltech-nlp/russe_detox_2022
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/sktwtrg/stanza_ja_test
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
https://github.com/smarton-empower/smarton-ai
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/smile-data/smile
https://github.com/socc-io/piqaboo
https://github.com/somiltg/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/somiltg/xquad
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/sonalisrijan/Deep-Learning-based-Defect-Prediction-Model-for-Source-Code
https://github.com/sonrisa07/transformer
https://github.com/sooftware/Attention-Implementation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.